Climate and More Sustainable Cities - NUS

Available online at www.sciencedirect.com

Procedia Environmental Sciences 1 (2010) 247–274

Climate and More Sustainable Cities: Climate Information for Improved Planning and Management of Cities (Producers/Capabilities Perspective) C.S.B. Grimmonda, , M. Rothb, T.R. Okec, Y.C. Aud, M. Beste, R. Bettse, G. Carmichaelf, H. Cleughg, W. Dabberdth, R. Emmanuelj, E. Freitasj, K. Fortuniakk, S. Hannal, P. Kleinm, L.S. Kalksteinn, C.H. Liuo, A. Nicksonp, D. Pearlmutterq, D. Sailorr and J. Voogts a

King’s College London, London, United Kingdom b National University of Singapore, Singapore University of British Columbia, Vancouver, Canada d Applied Climatologists, Marco Island, Florida e Met Office, Exeter, United Kingdom f University of Iowa, Iowa City, United States g Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, Australia h Vaisala, Louisville, Colorado, United States i Glasgow Caledonian University, Glasgow, United Kingdom j University of Sao Paulo, Sao Paulo, Brazil k University of Lodz, Lodz, Poland l Harvard University, Boston, United States m University of Oklahoma, Norman, Oklahoma n University of Miami, Coral Gables, Florida o University of Hong Kong, Hong Kong, China p Greater London Authority, London, United Kingdom q Ben-Gurion University, Negev, Israel r Portland State University, Portland, United States s University of Western Ontario, London, Canada c

________________________________________________________________________________________________ Abstract In the last two decades substantial advances have been made in the understanding of the scientific basis of urban climates. These are reviewed here with attention to sustainability of cities, applications that use climate information, and scientific understanding in relation to measurements and modelling. Consideration is given from street (micro) scale to neighbourhood (local) to city and region (meso) scale. Those areas where improvements are needed in the next decade to ensure more sustainable cities are identified. Highpriority recommendations are made in the following six strategic areas: observations, data, understanding, modelling, tools and education. These include the need for more operational urban measurement stations and networks; for an international data archive to aid translation of research findings into design tools, along with guidelines for different climate zones and land uses; to develop methods to analyse atmospheric data measured above complex urban surfaces; to improve short-range, high-resolution numerical prediction of weather, air quality and chemical dispersion through improved modelling of the biogeophysical features of the urban land surface; to improve education about urban meteorology; and to encourage communication across scientific disciplines at a range of spatial and temporal scales. Keywords: Urban climate; modelling; observations; human-environment interactions; adaptation; mitigation; built environment ___________________________________________________________________________________________________________ 1. Introduction 1.1 Sensitivity of cities to climate variability and change Cities and their inhabitants are key drivers of global climatic change. The large and ever increasing fraction of the world’s population that lives in cities uses a disproportionate share of resources and produces climate-altering atmospheric pollutants. Cities affect greenhouse gas sources and sinks both directly and indirectly. They are the main source of anthropogenic carbon dioxide emissions due to the burning of fossil fuel for heating and cooling, industrial processing, transport of people and goods and so forth. While the exact values are subject to debate, it is widely held that more than 70 per cent of anthropogenic carbon emissions can be attributed to cities, with the wealthy cities located in developed countries in the northern hemisphere as the main emitters [1][2]. (Svirejeva-Hopkins et al. [3] place this value in excess of 90 per cent.) Cities are also sources of many anthropogenic pollutants emitted to the atmosphere, with consequences for both local air quality and for regional and global atmospheric chemistry and its consequences for climate change [4]. Moreover, the demand for goods and resources by city dwellers, both historically and today, is a major driver of regional land-use change such as deforestation, surface pavements, buildings and drainage patterns.

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1878-0296 © 2010 Published by Elsevier doi:10.1016/j.proenv.2010.09.016

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C.S.B. Grimmond et al. / Procedia Environmental Sciences 1 (2010) 247–274

Cities are also sensitive to climate variability and change. They have the highest population densities (over 50 per cent of the world’s population is concentrated in less than 3 per cent of the land area) and many urban residents are poor and especially vulnerable to extreme events and climatic variability. Population density is even greater in low elevation coastal zones (LECZs; 20 W m-2 [154]. Reduced solar radiation in turn slows photochemical processes, reducing the formation of secondary pollutants in urban areas [155]. On the other hand, urban heat islands (increased air temperatures) accelerate photochemical processes and modify dispersion characteristics [112][113][156]157]. Surface materials and morphology alter the albedo (reflectivity) of the urban surface. Lighter materials tend to have higher albedos than darker building fabrics. Building materials (paints, roofing covers, etc), which have higher albedos, have been developed to reduce the radiative loading of urban areas and thus mitigate urban heat islands [158][159]. These so-called cool materials no longer have to be light coloured and are being used for all elements in the city, roofs and walls, and for vehicles as well [160]. If the surface materials are kept constant, a larger height-to-width ratio results in a lower bulk albedo for an urban array [161][162]. The net long-wave radiation depends on both the atmospheric (for Lp) and surface conditions (Ln). The urban atmosphere is both polluted and warm. In general the warmth of the urban heat island dominates and Lp is enhanced compared to that over the countryside. The surface materials and urban structure influence the surface temperature and emissivity. The trapping of long-wave radiation in areas with low sky view factors (large H/W ratios) results in a lower net long-wave loss (L*) at street level. While urban influences on individual radiative fluxes can be significant, and can vary substantially at the microscale or local scale, overall the effects tend to balance and the net all-wave radiative flux in cities tends to be close to that in nearby rural settings [163]. The turbulent sensible heat flux is driven by the net available energy, the gradient in air temperature between the surface and the air above it and the ability of the air to transport the energy away from the warm location (towards or away from the surface). Typically in cities, especially in summertime and in densely built up areas, unstable conditions prevail during daytime and mildly unstable or neutral conditions at night. At high latitudes in winter, or at night in areas of low building density, transport of sensible heat towards the surface may occur at night. The airflow regime, which is influenced by the surface morphology, can enhance or dampen heat transport to and from the surface. The typical diurnal course of the turbulent sensible heat flux is related to the nature of the building fabric (a key control on the storage heat flux) and the available moisture including the fraction of green space (key controls on the latent heat flux). Typically the storage heat flux is considerably larger in an urban area than its rural surroundings [164]. This flux is the net uptake or release of energy (per unit area and time) by sensible heat changes in the urban canopy air layer, buildings, vegetation and the ground. Key characteristics that influence the size of the storage heat flux are the surface materials, the urban structure and the resulting thermal mass. In general, urban surface materials have good ability to accept, conduct and diffuse heat into (and out of) the urban fabric. The flux is therefore significant because there is a large mass to heat up and cool down, plus there is a large surface area when vertical faces are included. In a rural area the soil heat flux may be about 5 per cent of the net all-wave radiation; in cities this value may be up to 40–50 per cent. Moreover, there is a distinctive diurnal trend in cities. The storage heat flux is typically larger in the morning, before solar noon, as heat is transported into the building volume. However, by mid- to late afternoon heat is transferred back to the surface and released into the atmosphere, which helps to maintain a positive turbulent sensible heat flux and hence unstable stratification in cities in the evening and at night. This large heat store also helps to increase the energy available for longwave radiative exchange and is a contributor to the characteristically warmer air temperature. Since conduction is not as efficient a process as convection, typically there are steep thermal gradients into the urban fabric relative to the surface temperature. Very high surface temperatures are frequently observed in cities (for example, by thermal remote sensing), but away from surfaces (for example, inside building cavities or air temperatures) the range of temperatures are considerably less. Thus the thermal characteristics (heat capacity, thickness of layers, density) of built materials provide opportunities for architects, planners and engineers to manipulate energy exchanges both internally and externally for a building, thereby affecting urban climates at the microscale and local scale. The complexity of the urban surface makes the storage heat flux difficult to observe. Two approaches have primarily been taken through (a) calculation as a residual of the energy balance, and (b) intensive sampling of the temperatures of all surface facets then calculation using heat conduction equations. Both approaches have the possibility to contain large measurement errors but recent comparisons of the two methods show consistency in their results [140][165]. Advection results from spatial differences of surface characteristics, for example in surface temperature, moisture availability or roughness. The city’s setting (for example, coastal or valley), dictates the magnitude and direction of these exchanges at scales larger than the city. Within the city, the patchiness of urban surfaces (at the property or neighbourhood scale) affects horizontal energy exchanges and mixing. For example, patchy vegetation may give contrasts of air temperature and moisture in close proximity, which

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can result in a net horizontal flux that cools warmer areas. On a hot summer’s day, well-irrigated grass next to a road or other paved surface will result in such advection. The effects of this on spatial variability of evapotranspiration rates in parks have been documented [166]. Such patchiness and advection have important implications for the stress of vegetation in urban settings. However, the size of the advective fluxes between neighbourhoods is not well documented. 3.4 Anthropogenic emissions (heat, water, carbon dioxide) Energy consuming activities in cities contribute to anthropogenic emissions of waste heat and carbon dioxide (CO 2) within the boundaries of the city and at the regional power plants that serve the city. These emissions result from energy use in buildings, transportation, manufacturing and other industry. The relative magnitude of each source varies as a function of the thermal climate of the city, the relative size of a city’s manufacturing sector and socio-economic and cultural factors that vary between cities and countries such as the mode of personal transport, the timing of the workday and the source of fuels. Based on aggregate energy consumption data [167][168] in developed nations, energy use is relatively equally distributed among the building, transport and manufacturing sectors, respectively. The way in which energy consumption translates into anthropogenic emissions, however, is different for each sector. For vehicles, the burning of gasoline or diesel fuel results in direct emission of heat and CO2 at the point of use of the vehicles and this results in a distinct diurnal profile with local peaks corresponding to morning and evening traffic rush hours. In contrast, energy consumption in the building and manufacturing sectors has both a direct local emissions component (within cities) and a primary emissions component at the source (the power plant delivering electricity to these sectors) which depends on the source of the power (wind, hydroelectric, fossil fuel or nuclear). 3.4.1 Estimating the magnitude of the anthropogenic heat flux (QF) Many early studies equated end-use of energy in buildings with direct emission of sensible heat into the urban environment [86][169]. There are two factors that may make estimates inaccurate. Firstly, larger commercial buildings in many cities rely on some form of evaporative cooling. As a result, any heat removed from a building (including the waste heat from energy use) leaves the building as a combination of sensible and latent heat fluxes. Secondly, heat rejected from buildings can be significantly different from energy consumption due to environmental loads (for example, direct solar radiation transmitted through windows). Such environmental loads are ejected by the air conditioning system regardless of the energy consumption within the building. Accurate anthropogenic emission estimates from buildings therefore require a whole-building energy balance. Over the past several decades researchers have tried to estimate anthropogenic heat emissions in cities [170][171]. These estimates range from simple annual estimates of city-wide heat emission to detailed lot-level measurements of energy consumption. Building energy simulation tools can estimate actual sensible and latent heat emission from cooling, heating and ventilation systems in buildings [172][173]. The different approaches to modelling QF clearly show that the magnitude of the flux depends upon the scale of analysis. While a city-wide estimate of anthropogenic heating may suggest the value is 10s of W m-2, it is possible at the scale of a city block or an individual building for the value to rise above 1 000 W m-2 [86]. There are very few cases, however, where researchers attempted to physically measure anthropogenic heat and/or moisture emissions. This is due in part to the variety of sources of waste heat and moisture in the urban environment and the challenges associated with measuring these emissions. Some researchers have used eddy covariance to estimate the total heat or moisture flux from urban canyons and then have combined these estimates with measurements and estimates of the individual flux terms in the energy budget to estimate anthropogenic heating as a residual in the energy balance [140]. While useful, this approach is also an indirect measurement of anthropogenic flux subject to errors from the uncertainties in each of the measured flux terms. 3.4.2 Estimating urban CO2 emissions Direct sources of urban carbon dioxide emissions include transport, households, the human body, soils and vegetation (the total urban ecosystem). The first attempts to quantify the role of these sources on the carbon budget have largely focused on inventories of emissions typically constructed through a bottom-up aggregation process that accounts for emission factors (often derived from laboratory or specific field measurements), activity levels (obtained from local authorities, specific surveys, roadway maps, aerial photographs, geographic statistics, etc.) and source distributions. In this way, emissions are computed through emission factors and activity levels (production, consumed fuel, distance travelled, etc.) for each emission source (using emission processors, such as SMOKE (Sparse Matrix Operator Kernel Emissions Modeling System. Center for Environmental Modeling for Policy Development (CEMPD), University of North Carolina, Chapel Hill (http://www.smoke-model.org/index.cfm)). Complete inventories must include emissions from mobile (for example, vehicles), area (for example, residences) point (for example, industries) and biogenic sources (for example, soils, vegetation). (See, for example, Nowak [174]; Jo and McPherson [175]; Mensink et al. [176].) Alternatively, surface–atmosphere exchanges of CO2 can be measured directly using micrometeorological techniques, notably eddy covariance equipment mounted on tall towers. Such direct measurements can be used to evaluate emission inventories and have the advantage of including all major industrial and mobile, minor commercial and residential sources from a specific region. These data are, however, still relatively rare. The most striking features common to most reported measurements are the positive (directed away from the surface) flux values during most hours of the day [153][177][178][179][180][181]. Thus cities are typically sources of CO2, unlike vegetated areas where photosynthesis results in assimilation (uptake) of CO 2 during daytime hours. Flux peaks, often visible in the early morning and late afternoon, may exceed 10 μmol m-2 s-1, and correspond to peaks in traffic volume during the rush hour periods. The magnitude of the fluxes in the middle of the day is strongly modulated by the amount of vegetation. Strong localized sources such as main road intersections can cause directional variability in the flux which may require careful source area analysis. The few long-term studies available show seasonal variability and the expected increase of CO 2 fluxes during the winter months caused by higher emissions from increased space heating and reduced uptake by vegetation outside the growing season [178][180].


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Seasonal differences can be very large in cold climate regions. For example in Copenhagen average monthly winter values are up to six times larger than summer emissions at a central city site [182]. 3.5 Moisture, water and hydrology Since the turbulent latent heat flux (QE) is the energy equivalent of the evaporation term in the water balance, the size of this flux influences not only the partitioning of the convective energy fluxes (Bowen ratio, β = QH/QE) and therefore many of the urban climate features observed, but also other fluxes in the water balance such as groundwater recharge. The turbulent latent heat flux depends on the availability of moisture at the surface, the sign and size of the surface–air humidity gradient and the ability of the atmosphere to transport moisture. Unlike the spatial pattern of surface temperatures in a city where differences are always present, albeit with varying contrasts, in urban environments it is possible to have areas/times where there is no surface moisture (for example, a sealed parking lot with no vegetation after a long period with no rain) and areas where it is freely available (for example, irrigated parks, detention ponds). Human activities such as street cleaning, allowing/banning/regulating garden irrigation, etc., can significantly modify water availability and thus rates of QE. When irrigation bans are instituted to conserve water, the rate of evapotranspiration drops accordingly. Typically the densely developed central area of a city has little vegetation, and residential areas much more, a difference that is reflected in their patterns of QE. However, even in the driest urban settings such as central Mexico City and Ouagadougou water is present and evaporation is measurable [140][183]. Of course, evaporation is also influenced by the frequency and intensity of precipitation events, and the efficiency of methods used to detain or rapidly drain rainwater. In many cities water is retained in neighbourhood detention ponds (particularly common in the United States) or recycled into local wetlands, or held on individual properties (becoming common in Australia) to irrigate vegetation or for internal water use. Under very humid conditions small moisture gradients can limit evaporation rates. Immediately following a rain shower, or in the early morning after dewfall, there can be large latent heat flux values for a short period of time (for example, Richards and Oke [184]; Richards [185]). Depending on the climate and season, precipitation occurs in different forms including rain, hail and snow. It is important to observe and record the amount, form and intensity of precipitation in cities because of its relevance to management and safety issues such as flooding, hail damage, drought and the need for road clearance. Until recently observations from tipping bucket raingauges have been the primary source of precipitation data but now new sensors are becoming available including a sensitive pan. Point measurements do not afford sufficient spatial, and in certain circumstances temporal, resolution so new approaches are in use. These include radar which provides spatial information but cannot be used alone due to uncertainties about its accuracy [186][187]. These uncertainties stem from errors due to surface clutter, beam attenuation and measurement height relative to the surface, among other factors [188]. Satellite-based observations using the Tropical Rainfall Measuring Mission (TRMM) have been used to study the extent of rainfall modification [189]. The role of urban areas in modifying rainfall continues to be disputed [6][190]. There is a need for more extensive measurements in urban areas. There have been very few studies of urban dew although enhanced moisture within the urban atmosphere as a result of anthropogenic activity suggests the potential for accumulation. Significant amounts (0.1 and 0.3 mm day-1) were found using scale models in Vancouver, Canada, on nights with optimal conditions for dew formation especially on roofs and exposed grass [191]. The spatial patterns of atmospheric moisture in cities are influenced by those of temperature, and by the surface moisture and latent heat fluxes. Typically, urban areas are described as having an atmospheric urban moisture deficit compared to surrounding rural areas (for example, studies cited in Kuttler et al. [192]). This is because of less vegetation, the greater air temperatures (which increase the saturation vapour pressure) and drainage networks designed to rapidly remove precipitation from urban areas. Nevertheless, urban air can contain greater moisture at night and in the winter, especially at high latitudes. Care needs to be taken when comparing moisture metrics as there are a number of different measures (for example, relative humidity, specific humidity, dew point temperature, absolute humidity, vapour pressure) and these may be a function of other variables (for example, pressure, temperature) as well as actual moisture content change in the air. 4. Prediction and modelling capabilities 4.1 Scale models Scale models using a wide variety of experimental facilities and measurement techniques have contributed significantly to understanding the urban atmosphere. Most commonly urban flow, turbulence and dispersion phenomena have been simulated in wind tunnels and water flumes or outdoors over idealized arrays of building-like obstacles. Attention has focused on the RSL where practical measurement issues limit comprehensive full-scale investigations. Most laboratory studies have been conducted under neutral stratification and/or strong flow when mechanical turbulence dominates. Several scale-model studies have focused on development of morphometric methods to relate roughness length and displacement height to characteristics of building structures (for example, Bottema [193][194]; Macdonald et al. [195]). These methods have been reviewed and their merits evaluated using both full-scale and scale-model data [196]. Extensive wind-tunnel datasets of flow and dispersion characteristics inside and above different types of building arrangements have been used to develop a semi-empirical urban dispersion model and to develop a classification scheme for urban building zones [196][197]. The recent development of computational fluid dynamics (CFD) codes for cities (Section 4.3) has spurred the need for highresolution laboratory datasets to evaluate them [198][199][200][201][202]. Different types of simulation domains and approaches are available for street canyon studies (for example, Vardoulakis et al. [203], Kastner-Klein et al. [204]) Of particular interest are street canyons that are characterized by long buildings flanking narrow streets, for which the flow and mixing inside the street is driven by a recirculation-type quasi-two-dimensional vortex [203].

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Both wind-tunnel and full-scale observations, however, show that vortices that develop at the lateral building edges can extend over significant portions of street canyons and the more simple two-dimensional flow regimes often depicted are not necessarily typical [64][127][143][144]. Studies have demonstrated that small-scale features such as roof shape, the presence and placement of trees or moving traffic can significantly impact street canyon ventilation rates [64][199][205][206][207][208][209]. Similarity considerations imply that only a small range of model scales can be investigated in wind tunnels given their typical size. The few studies of realistic models of cities highlight that variability of the building height has a strong influence on the flow and turbulence structure within the urban RSL [64][127][210][211]. Wakes and eddies behind high-rise buildings can cause rapid vertical mixing of high momentum fluid and dominate the UCL–UBL interactions. Profiles of turbulence statistics, observed inside and above urban canopies, show high spatial variability horizontally and vertically. The turbulence kinetic energy (TKE) and turbulent shear stress have low values inside the UCL and pronounced peaks in the shear layer region developing above roof level. This is in good agreement with results for vegetation canopies and similar results of RSL turbulence found in full-scale urban studies (Section 3.2). 4.2 Statistical models Statistical models allow easy estimation of the effects of cities on climate. Their main advantages are simplicity, which allows for approximation of meteorological parameters within the city; low computational requirements; a modest number of input parameters; and low risk of producing unrealistic results. The disadvantages of many (but not all) statistical models include limitation to the city (region, climate zone) in which they were developed; need for long observation periods or data from a large number of different locations; and lack of a physical basis. The canopy urban heat island is the most intensively studied phenomenon and therefore many statistical models have been developed [212][213][214][215][216][217][218][219][220][221][222][223][224][225][226][227]. Many are simple linear (multiple) regressions of temperature differences between the city centre and the surrounding rural area, ΔT, as a function of meteorological variables such as wind speed, rural lapse rate, etc. [212][228][229][230][231][232][233]. More sophisticated statistical models use spectral analysis, eigenvectors or neural networks [233][234][235][236]. They often work well for the city in which they were developed and can be useful for local authorities to predict risks related to UHI (like amplification of heatwaves by that city). Statistical relations have been used to determine the normalized UHI evolution which allows for fast (operational) forecasts of city temperatures [215]. This has been used to predict future climate conditions (with global or regional climate model outputs), and to correct historical records for urban effects. Very similar statistical relations with inputs including land cover information (for example, built fraction), have been developed to model intra-urban temperature distributions [237][238][239][240][241][242]. Statistical models of a city’s influence on other meteorological parameters are sparse and in the majority of cases reduced to regression analysis against meteorological, topographical or urban parameters. Statistical models of radiation fluxes have proved fairly useful [243][244][245][246]. Availability of incoming short-wave radiation allows modelling of net all-wave radiation by simple linear regression. Net and upward short-wave fluxes can be estimated if the surface albedo is specified. Absorbed short-wave radiation can be used in correction factors to calculate upward long-wave radiation when the surface temperature is replaced by standard near-ground air temperatures [247]. Turbulent heat fluxes can be expressed as a fraction of net all-wave radiation and the storage heat flux as a derivative of net radiation to capture hysteresis phase-lags between the two fluxes [248][249]. The objective hysteresis model (OHM) is a generalization of this approach which incorporates both the hysteresis nature of the storage heat flux and the surface properties of the city [164][250]. These models form part of the local-scale urban meteorological parameterization scheme (LUMPS) which requires only meteorological parameters (solar radiation, air temperature and humidity, atmospheric pressure) and surface descriptors to model turbulent (sensible and latent) and storage fluxes [247][251]. 4.3 Numerical Models 4.3.1 Computational Fluid Dynamics Computational Fluid Dynamics (CFD) models cover a wide range of numerical models from Reynolds-averaged Navier-Stokes (RANS), through Large Eddy Simulation (LES) to Direct Numerical Simulation (DNS) in terms of increasing computer needs. These models make a variety of assumptions to allow flows to be calculated at the microscale. There are now commercial CFD codes available which have been used to simulate flow over and inside street canyons with additional modifications to examine the transport of reactive air pollutants [252][253][254][255][256]. Examples are CFD-Urban, FLACS, Fluent-EPA, FEFLO, and FEM3, which were included in a CFD model comparison exercise for Manhattan by Hanna et al. (2006a, 2006b). Model evaluation efforts and inter-comparison studies for European CFD models are summarized in Sahm et al. [258] and Ketzel et al. [257]. Most CFD models use the RANS equations, which imply a steady-state solution [257][258](Hanna et al., 2006a, 2006 The CFD models require input of a good meteorological profile on the upwind edge of their geographic domain, which is usually 1 to 5 km on a side. It is found that there is a need to adjust model parameters to assure that sufficient turbulence is generated by the model, and to account for meandering of the input wind field. The urban-adjusted CFD models do produce reasonable agreement with field observations and allow the flow patterns around specific buildings to be seen, such a large eddies in the lee of tall buildings. Large Eddy Simulation is becoming more widely used. In the LES approach, the large-scale, energy-carrying eddies are explicitly resolved under unsteady and intermittent flow conditions while smaller scale eddy activity is parameterized. Initial applications were restricted to flow in simple two-dimensional street canyon structures. Large Eddy Simulation studies have modelled pollutant flux transport along a street canyon at roof level and used wind tunnel measurements to assess performance [259][260][261][262]. Pollutant exchange rates, pollutant concentration and retention times to compare ventilation and air quality conditions in street canyons over a range of aspect ratios have been calculated [263][264][265][266][267]. Large Eddy Simulation produces realistic


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results for flow and pollutant transport in narrow, simple two-dimensional street canyons (aspect ratio between about 0.3 and 10, that is, skimming flow) under neutral conditions, including simple air pollution chemistry (NO-NO2-O3 mechanism). Improved computer power has led to the development of LES models capable of dealing with simple three-dimensional built configurations (for example, So et al. [268]). Large Eddy Simulation has been used to investigate the role of plan area density (λp) within three-dimensional street canyon mean wind speeds [269]. Wind speeds were found to be 2–3 times higher in the sparse urban configuration. Similarly, LES has been used to compare flow in aligned and staggered arrays of cubes, with varying densities and building heights [270][271]. Drag in the staggered arrays is sensitive to λp but not for aligned ones. Moving from uniform to nonuniform building height drastically increases the drag and changes the flow coherence [272]. Direct Numerical Simulation approaches are also beginning to be pursued [273][274][275]. The complicated nature of real world cities has constrained the number of numerical studies of flow over complex and realistic geometry [272][276][277][278][279]. Solving the flow explicitly in the canopy layer remains challenging due to computer memory and speed demands. One approach that is currently being actively developed is that of nesting CFD codes into mesoscale models [280][281]. At the mesoscale, parameterizations have been included to model the effect of land surface on the dynamics in the Atmospheric Boundary Layer by LES [282][283]. 4.3.2 Parameterizations Increases in computing capability, which allow greater spatial resolution within numerical models, have been accompanied by a surge in the number of parameterizations of urban surface–atmosphere exchanges. Many groups interested in different applications have developed models to incorporate urban features, ranging from global climate modelling, numerical weather prediction, air quality forecasting and dispersion modelling to characterizing measurements, understanding heat island circulations and water balance modelling [56][58][66][67] [284][285][286][287] [288][289][290][291][292][293][294]. There are more than 40 schemes incorporating a wide range of urban features such as surface morphology, presence of impervious materials, vegetation cover and anthropogenic heat, all of which have a significant effect on the urban climate and need to be included, if the computational requirements are not excessive. These models can be classified in a number of ways including which fluxes they actually calculate [295][296]. Many of the urban land surface parameterizations have been evaluated against observations (for example, Grimmond and Oke [251]; Masson et al. [297]; Dupont and Mestayer [298]; Hamdi and Schayes [299]; Krayenhoff and Voogt [293]; Kawai et al. [300]). Currently an international model comparison is being conducted, in a controlled manner, that allows robust model intercomparison by class [295][296]. Early results demonstrate that inclusion of vegetation is important, even in dry environments. Models have greatest ability to model net all-wave radiation. Participation in the model comparison has resulted in improved simulation capability in these models in general, which points to the importance of such international collaborations. 4.4 Dispersion and air quality models Improving public health and safety of urban dwellers requires improved ability to characterize and predict urban air quality (chemical weather) which, like physical processes, is impacted by variability at a range of space and timescales [301]. This involves both observations and models, and their close integration. The use of chemical weather forecasts in public health and safety management is new. For example, Air Quality Forecasting System for Australian cities (AAQFS) since 2000; RAMS/BRAMS has run operationally in Brazil since 2004 and more recently in other South American countries (for example, Peru and Chile) [302]. Many cities around the world provide real-time air quality/chemical weather forecasts, and several national services are broadening to include prediction of environmental phenomena such as plumes from biomass burning, volcanic eruptions, dust storms and urban air pollution that could potentially affect the health and welfare of their inhabitants. Such alerts can help reduce acute exposure when high pollution levels are expected. Routine daily forecasts enable the public to make healthier choices (for example, exercising outside only on low pollution days). Chemical weather forecasts enable business organizations to schedule their activities more effectively to reduce emissions on predicted high-pollution days, to reduce the cost of continuous emission controls. Urban dispersion models range from very simple single equation models that parameterize the urban boundary layer and its controls on dispersion, to very complex CFD models that have the potential to calculate with high precision and resolution. Applications range from estimates of long-term health effects to short-term emergency response. The spectrum of models is widening rapidly due to the demand, the availability of tracer observations to test the models and the availability of three-dimensional high resolution (< 1 m) information on urban geometry. The complexity of urban dispersion models has been driven by computer speed and storage capacity. Early Gaussian plume models applied to cities used rural parameterizations of stability, adjusted towards neutral conditions and larger dispersion coefficients [303][304]. Other simple adjustments for urban land use have included increased surface roughness length, decreased albedo, etc. These approaches have been used in gridded meteorological models such as the fifth generation National Center for Atmospheric Research/Pennsylvania State University Mesoscale Model (MM5) and urban airshed models to study ozone formation [305]. Today, urban dispersion is simulated typically using CFD models, models generating a mass-consistent wind field and using a Lagrangian particle dispersion model (LPDM), street-canyon models designed to reproduce the complex dispersion patterns observed in narrow street canyons or Gaussian plume models using specific building geometry [156][157][195][257][258][306][307][308][309][310]. Other dispersion models parameterize the urban surface and boundary layer, include urban wind profiles or combine with threedimensional meteorological forecast models [312][313][314][315][316]. Simple Gaussian-based urban dispersion models also parameterize low wind speeds, high turbulence intensities and the known tendency towards neutral conditions in urban areas [317][318][319][320][321]. They perform adequately against urban field observations of tracer dispersion and often perform as well or better than more complex models [322].

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All dispersion models should incorporate a meteorological model or parameterization scheme to account for ubiquitous urban effects such as the slowing of winds, the increase of turbulence, the vertical profile of meteorological variables in the urban canopy layer, and the tendency towards neutrality due to mechanical mixing. The thermal structure and depth of the urban mixing layer is also needed for plumes that have several kilometres of travel or are released above the urban canopy. Ideally urban dispersion models should also be able to account for changes in meteorological conditions between different urban districts. 5. Conclusions and assessment of gaps and recommendations for the next decade This paper focuses on current capabilities to observe and predict urban atmospheric processes across a range of spatial scales. A wide range of applications use urban meteorological information. These range in scale from architectural design of the individual building to the whole city and its impact on the region, and to the role that cities and their inhabitants have on global changes in atmospheric composition and climate variability. The data needs, predictions and process understanding range from the protection of the inhabitants from short-term meteorological events such as intense rainfall through extremes of weather such as caused by heat stress enhanced by the urban heat island, and on to the long-term impacts of building design and urban planning and the role of transportation network design on air quality and health. Thus there are important social, economic and health benefits of an enhanced understanding of urban meteorological processes from the timescale of seconds (for example, chemical dispersion) to 100 years (for example, the lifetime of buildings) to 1 000 years (for example, city-scale planning). Awareness of current scientific capabilities and understanding based on observations and modelling is essential. Here these are reviewed and the main areas where improvements in our capabilities are needed for the design of more sustainable cities are identified. The following are identified as areas where improvements in our capabilities are needed to ensure that in the next 10 years we actively move towards developing more sustainable cities. Each is given a high (H), medium (M) or low (L) ranking. Observations (a)

Need operational urban meteorological networks (within and around the city) with optimum balance between resolution and practicability, networks that include surface-based instrumentation (soil moisture and air/soil/surface temperature), and vertical profiles (from within the deep urban canopy layer to the top of the boundary layer) of temperature, humidity, wind, turbulence, radiation, rainfall, air quality (gases and particles, precursors and secondary), reflectivity and refractivity. (H)

(b)

Need observations over and within a larger range of urban morphologies to establish universal flow and flux characteristics. Need to ensure that there are long-term datasets (rather than short-term campaigns) that have wide spatial representativeness. The existing long-term measurement stations should be preserved. (H)

(c)

Need to measure fluxes of CO2 using eddy covariance approach combined with isotopic analysis to determine not only the sizes of these fluxes but also to identify emission sources (for example, background concentration, gasoline combustion, natural gas combustion and respiration) to evaluate the role of cities on the earth–atmosphere carbon exchange. (H)

(d)

Need to undertake measurement studies to validate quantitative estimates of anthropogenic heat and moisture emissions and improve estimation techniques at a range of scales starting with the individual building where measurements can close the energy budget of a control volume. (M)

(e)

Need simultaneous measurements of flow properties at various sites and levels to better study coherent structures and intermittent ventilation processes within the RSL. (M)

(f)

Need to better assess urban surface characteristics (for example, emissitivity to develop methods to correct for thermal anisotropy), and determine fluxes from remote sensing. (M)

(g)

Need to explore the use of new measurement techniques including the use of remote-sensing technologies and smaller, more mobile and affordable instruments. (M)

Data (a)

Need to meet data requirements to allow translation of research findings into urban/building design tools and guidelines for different climate zones and classes of urban land use. (H)

(b)

Need to ensure that data are provided in a format that is usable for a broad range of practitioners without compromise to scientific accuracy and integrity. (H)

(c)

Need to ensure metadata to describe instrument, siting, quality assurance and control features and documentation are complete and comparable by creating and using a standardized urban protocol. (H)

Understanding (a)

Need to develop methods and frameworks to analyse atmospheric data measured above complex urban surfaces. This includes measurement source areas to ensure representative results and meaningful comparison between sites. (H)

(b)

Need to know more about the outer layer of the UBL, that is, the atmosphere above the ISL. (H)

(c)

Need to assess for each intervention what scale interventions are needed and possible (for example, legally, economically, planning, technically, etc.) to make cities more sustainable (liveable, healthy, etc.). (H)


259

(d)

Need for assessment of human-induced large-scale climate change at the scale of cities to ensure that the signal of climate change is distinguished from the noise of natural variability. (H)

(e)

Need to better understand the coupling of surface and air temperatures. (M)

(f)

Need to examine ventilation and pollutant removal mechanisms (upward and sideward) for three-dimensional street canyons. (M)

(g)

Need to understand if urban canopies are a special class of rough wall or canopy flows, and to what extent urban RSL turbulence can be described with a possibly modified mixing layer model. (M)

(h)

Need to increase our knowledge on the subsurface heat island. (L)

Modelling (a)

Need to evaluate urban land surface schemes in both offline and online mode for a wide range of conditions to ensure that the models are fit for purpose. (H)

(b)

Need to improve short-range, high-resolution numerical prediction of weather, air quality and chemical dispersion in the urban zone through improved modelling of the biogeophysical features of the land surface and consequent exchange of heat, moisture, momentum and radiation (the surface energy balance) with the UBL. (H)

(c)

Need CFD/LES studies of wind and pollutant transport in regimes other than skimming flow and with combined effects of wind and buoyancy. (H)

(d)

Need to improve understanding of feedback mechanisms between the urban environmental conditions and human activity. (H)

(e)

Need to incorporate more realistic air pollution chemistry mechanisms (for example, O3 titration at urban canopy level) into models. (M)

(f)

Need to further develop multiscale modelling to allow investigations such as the effect of large-scale atmospheric turbulence on the neighbourhood or microscale turbulence below the canopy levels; the interaction between natural and artificial landscapes; the assessment of street-level comfort; building energy consumption; and urban design. (M)

(g)

Need laboratory and CFD/LES studies with structures that more closely resemble cities than earlier, idealized homogenous arrays to inform model development for urban RSL turbulence. (M)

(h)

Need further work on a simple universal UHI model for applied users (for example, extensions from Oke [215]). (L)

Tools (a)

Need to develop tools to allow models to be able to accommodate the wide differences in data availability depending on the application from research to operational situation. For example, in field research studies, extensive wind observations may be available (and detailed building morphology), but for emergency response situations only minimal inputs may be available (for example, winds from the nearest airport, no three-dimensional building data). (H)

(b)

Need to develop designs that promote shading and ventilation without compromising air quality and natural lighting for hot cities. (H)

(c)

Need to encourage development of active simulation tools (for example, www.susdesign. com/tools.php) through community participation (for example, forums, blogs, wikis). (H)

(d)

Need to develop tools that allow competing and unintended impacts of proposed sustainable design to be assessed (for example, will urban greening reduce temperatures but increase humidity, resulting in no net increase in comfort levels?). (H)

(e)

Need to develop tools that allow assessment of the best, or the ranking of, social, economic and environmental decisions for urban climate management (for example, urban greening vs. repaving roads and pavements with high(er) albedo vs. low-emissivity materials vs. limiting the contribution of anthropogenic heat; investment in expensive multi-functional solutions (for example, vegetated roofs) vs. cheaper, single benefit solutions such as cool roofs). (H)

(f)

Need to make use of spatial and temporal estimation of transport emissions through vehicle fleet efficiencies and traffic data. (M)

(g)

Need to solve technical challenges such as moisture seepage in vegetated roofs, and hazards to street trees (for example, pests, new pathologies, soil quality, compaction, drainage, frequent disturbances from utility trenches and excessive paving). (M)

(h)

Need to determine how to link the beneficiaries of urban climate interventions with the costs of implementing them. (M)

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Education (a)

Need to ensure widespread education of the meteorological community of the needs for planning and managing cities of all sizes in an as sustainable manner as possible. (H)

(b)

Need to encourage communication which crosses traditional scientific discipline and spatial scale (for example: http://www.conservationeconomy.net ; http://www.sustainable-buildings. org/index.php). (H)

(c)

Need to improve public education and communication of heat/health perception through use of simple language and community access. (H)

(d)

Need for collaboration with stakeholders in the widespread development of heat/health warning systems. (H)

(e)

Need to communicate through conventional publications and to use current (and evolving) electronic media to allow accessibility with depth of content that is up-to-date. (M)

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